Unconstrained palmprint as a smartphone biometric

Ungureanu, Adrian-Stefan; Thavalengal, Shejin; Cognard, Timothée E.; Costache, Claudia; Corcoran, Peter

doi:10.1109/tce.2017.014994

Cited by 30 publications

(15 citation statements)

References 39 publications

(45 reference statements)

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“…Such global methods do not account for local variations in rotation and translation; therefore, they exhibit high recognition accuracies only on databases captured under partially constrained conditions, for example, using touch-based procedures. With the increasing popularity of touchless and less-constrained palmprint recog-nition systems [37], [38], several recent methods have been developed with a focus on local texture descriptors, which are more robust to local changes in rotation and illumination and therefore achieve higher recognition accuracies on touchless palmprint databases than coding-based methods do [2].…”

Section: A Coding-based Approachesmentioning

confidence: 99%

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Genovese

Piuri

Plataniotis

et al. 2019

IEEE Trans.Inform.Forensic Secur.

179

131

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Touchless palmprint recognition systems enable high-accuracy recognition of individuals through less-constrained and highly usable procedures that do not require the contact of the palm with a surface. To perform this recognition, methods based on local texture descriptors and Convolutional Neural Networks (CNNs) are currently used to extract highly discriminative features while compensating for variations in scale, rotation, and illumination in biometric samples. In particular, the main advantage of CNN-based methods is their ability to adapt to biometric samples captured with heterogeneous devices. However, the current methods rely on either supervised training algorithms, which require class labels (e.g., the identities of the individuals) during the training phase, or filters pretrained on general-purpose databases, which may not be specifically suitable for palmprint data. To achieve a high recognition accuracy with touchless palmprint samples captured using different devices while neither requiring class labels for training nor using pretrained filters, we introduce PalmNet, which is a novel CNN that uses a newly developed method to tune palmprintspecific filters through an unsupervised procedure based on Gabor responses and Principal Component Analysis (PCA), not requiring class labels during training. PalmNet is a new method of applying Gabor filters in a CNN and is designed to extract highly discriminative palmprint-specific descriptors and to adapt to heterogeneous databases. We validated the innovative PalmNet on several palmprint databases captured using different touchless acquisition procedures and heterogeneous devices, and in all cases, a recognition accuracy greater than that of the current methods in the literature was obtained.

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Section: A Coding-based Approachesmentioning

confidence: 99%

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Genovese

Piuri

Plataniotis

et al. 2019

IEEE Trans.Inform.Forensic Secur.

179

131

View full text Add to dashboard Cite

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“…ere are also some examples of using palmprint biometrics in a mobile scenario [45][46][47]. It is clear that implementing a palmprint-based biometric system using a smartphone may be successful even though it carries some difficulties: complex background, changing illumination, hand pose variation, and last but not least a limited processing power [48].…”

Section: Related Workmentioning

confidence: 99%

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Giełczyk

Choraś

Kozik

2019

Mobile Information Systems

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Palmprint biometrics is a promising modality that enables efficient human identification, also in a mobile scenario. In this paper, a novel approach to feature extraction for palmprint verification is presented. The features are extracted from hand geometry and palmprint texture and fused. The use of a fusion of features facilitates obtaining a higher accuracy and, at the same time, provides more robustness to intrusive factors like illumination, variation, or noise. The major contribution of this paper is the proposition and evaluation of a lightweight verification schema for biometric systems that improves the accuracy without increasing computational complexity which is a necessary requirement in real-life scenarios.

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“…The large heterogeneity of the mobile-embedded sensors and the rapid release of new models are not without consequences for mobile biometrics. As data comes from different acquisition sources, we face the challenge of crosssensor recognition, which has been investigated for palmprint [70] and periocular [19]. Similar studies also are necessary for other traits.…”

Section: Uni-modal Approachesmentioning

confidence: 99%

Recent Advances in Biometric Technology for Mobile Devices

Das

Galdi

Han

et al. 2018

2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS)

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The prevalent commercial deployment of mobile biometrics as a robust authentication method on mobile devices has fueled increasingly scientific attention. Motivated by this, in this work we seek to provide insight on recent development in mobile biometrics. We present parallels and dissimilarities of mobile biometrics and classical biometrics, enumerate related strengths and challenges. Further we provide an overview of recent techniques in mobile biometrics, as well as application systems adopted by industry. Finally, we discuss open research problems in this field.

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Unconstrained palmprint as a smartphone biometric

Cited by 30 publications

References 39 publications

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

PalmNet: Gabor-PCA Convolutional Networks for Touchless Palmprint Recognition

Lightweight Verification Schema for Image-Based Palmprint Biometric Systems

Recent Advances in Biometric Technology for Mobile Devices

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